# Polarimetric PatchMatch Multi-View Stereo

arXiv: https://arxiv.org/abs/2311.07600 · HTML: https://mlsanity.com/paper/2311.07600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [PatchmatchNet: Learned Multi-View Patchmatch Stereo](https://mlsanity.com/paper/2012.01411/) (2020)
- [DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch](https://mlsanity.com/paper/1909.05845/) (2019)
- [Multi-Scale Geometric Consistency Guided Multi-View Stereo](https://mlsanity.com/paper/1904.08103/) (2019)
- [Planar Prior Assisted PatchMatch Multi-View Stereo](https://mlsanity.com/paper/1912.11744/) (2019)
- [PANDORA: Polarization-Aided Neural Decomposition Of Radiance](https://mlsanity.com/paper/2203.13458/) (2022)
- [TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo](https://mlsanity.com/paper/1903.10929/) (2019)
- [Polarimetric Multi-View Inverse Rendering](https://mlsanity.com/paper/2007.08830/) (2020)
- [PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility](https://mlsanity.com/paper/2108.08943/) (2021)
- [Multi-View Azimuth Stereo via Tangent Space Consistency](https://mlsanity.com/paper/2303.16447/) (2023)

## Built on by (most cited first, 3 of 3)

- [PISR: Polarimetric Neural Implicit Surface Reconstruction for Textureless and Specular Objects](https://mlsanity.com/paper/2409.14331/) (2024)
- [Glossy Object Reconstruction with Cost-effective Polarized Acquisition](https://mlsanity.com/paper/2504.07025/) (2025)
- [PolarGS: Polarimetric Cues for Ambiguity-Free Gaussian Splatting with Accurate Geometry Recovery](https://mlsanity.com/paper/2512.00794/) (2025)

## Abstract (arXiv)

PatchMatch Multi-View Stereo (PatchMatch MVS) is one of the popular MVS approaches, owing to its balanced accuracy and efficiency. In this paper, we propose Polarimetric PatchMatch multi-view Stereo (PolarPMS), which is the first method exploiting polarization cues to PatchMatch MVS. The key of PatchMatch MVS is to generate depth and normal hypotheses, which form local 3D planes and slanted stereo matching windows, and efficiently search for the best hypothesis based on the consistency among multi-view images. In addition to standard photometric consistency, our PolarPMS evaluates polarimetric consistency to assess the validness of a depth and normal hypothesis, motivated by the physical property that the polarimetric information is related to the object's surface normal. Experimental results demonstrate that our PolarPMS can improve the accuracy and the completeness of reconstructed 3D models, especially for texture-less surfaces, compared with state-of-the-art PatchMatch MVS methods.